NDSS

WINNIE : Fuzzing Windows Applications with Harness Synthesis and Fast Cloning

Jinho Jung (Georgia Institute of Technology), Stephen Tong (Georgia Institute of Technology), Hong Hu (Pennsylvania State University), Jungwon Lim (Georgia Institute of Technology), Yonghwi Jin (Georgia Institute of Technology), Taesoo Kim (Georgia Institute of Technology)

Fuzzing is an emerging technique to automatically validate programs and uncover bugs. It has been widely used to test many programs and has found thousands of security vulnerabilities. However, existing fuzzing efforts are mainly centered around Unix-like systems, as Windows imposes unique challenges for fuzzing: a closed-source ecosystem, the heavy use of graphical interfaces and the lack of fast process cloning machinery.

In this paper, we propose two solutions to address the challenges Windows fuzzing faces. Our system, WINNIE, first tries to synthesize a harness for the application, a simple program that directly invokes target functions, based on sample executions. It then tests the harness, instead of the original complicated program, using an efficient implementation of fork on Windows. Using these techniques, WINNIE can bypass irrelevant GUI code to test logic deep within the application. We used WINNIE to fuzz 59 closed-source Windows binaries, and it successfully generated valid fuzzing harnesses for all of them. In our evaluation, WINNIE can support 2.2x more programs than existing Windows fuzzers could, and identified 3.9x more program states and achieved 26.6x faster execution. In total, WINNIE found 61 unique bugs in 32 Windows binaries.